Learning Timed Automata via Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Misc{Tappler:2019:arxiv,

• author = "Martin Tappler and Bernhard K. Aichernig and Kim Guldstrand Larsen and Florian Lorber",
• title = "Learning Timed Automata via Genetic Programming",
• howpublished = "arXiv",
• year = "2019",
• month = "15 " # feb,
• edition = "v3",
• keywords = "genetic algorithms, genetic programming, software engineering, timed automata, automata learning, model learning, model inference",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/corr/corr1808.html#abs-1808-07744",
• URL = "http://arxiv.org/abs/1808.07744",
• size = "11 pages",
• abstract = "Model learning has gained increasing interest in recent years. It derives behavioural models from test data of black-box systems. The main advantage offered by such techniques is that they enable model-based analysis without access to the internals of a system. Applications range from fully automated testing over model checking to system understanding. Current work focuses on learning variations of finite state machines. However, most techniques consider discrete time. In this paper, we present a method for learning timed automata, finite state machines extended with real-valued clocks. The learning method generates a model consistent with a set of timed traces collected by testing. This generation is based on genetic programming, a search-based technique for automatic program creation. We evaluate our approach on 44 timed systems, comprising four systems from the literature and 40 randomly generated examples.",
• notes = "Fig. 6. Learned model of the car alarm system (CAS).",

}

Genetic Programming entries for Martin Tappler Bernhard K Aichernig Kim Guldstrand Larsen Florian Lorber

Citations